97 Things Every Data Engineer Should Know

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.

Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.

Topics include:

• The Importance of Data Lineage - Julien Le Dem
• Data Security for Data Engineers - Katharine Jarmul
• The Two Types of Data Engineering and Data Engineers - Jesse Anderson
• Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy
• The End of ETL as We Know It - Paul Singman
• Building a Career as a Data Engineer - Vijay Kiran
• Modern Metadata for the Modern Data Stack - Prukalpa Sankar
• Your Data Tests Failed! Now What? - Sam Bail

Tobias Macey hosts the Data Engineering Podcast and Podcast.\_\_init\_\_ where he discusses the tools, topics, and people that comprise the data engineering and Python communities respectively. His experience across the domains of infrastructure, software, cloud, and data engineering allows him to ask informed questions and bring useful context to the discussions. The ongoing focus of his career is to help educate people, through designing and building platforms that power online learning, consulting with companies and investors to understand the possibilities of emerging technologies, and leading teams of engineers to help them grow professionally.